CSE5519 Advances in Computer Vision (Topic A: 2023 - 2024: Semantic Segmentation)
Segment Anything
Novelty in Segment Anything
Brute force approach with large scale training data (400x) more
Dataset construction
- Model-assisted manual annotation
- Semi-automatic annotation
- Automatic annotation (predict mask for 32x32 patches)
Tip
This paper shows a remarkable breakthrough in semantic segmentation with a brute force approach using a large scale training data. The authors use a transformer encoder to get the final segmentation map.
I’m really interested in the scalability of the model. Is there any approach to reduce the training data size or the model size with comparable performance via distillation or other techniques?
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